A delicious sandwich. A sizzling steak. A smoky sausage outside on the grill just about ready to split. There aren't many things that a delicious meat dish can't do when it comes to satisfying hunger. Marvelous meat can come in all different shapes, sizes, and flavours. All you have to do is choose your favourite!
However, when making your selection, you need to be careful where you go! You need to know about the finest selections for your money, and you need to know where to go to get the finest cuts.
At the Orillia Bakery & Deli, we think we can help!
You see, we offer the best of everything when it comes to meat. Our experts smoke all the meat we sell, in-house, and provide an experience unlike any other. 90% of our meat products are gluten-free to help with those allergies, and our varieties range from quick cold cuts all the way to full dinner entrees!
Need our meats for a special occasion? Call us! We'll set up customized party platter packages that will ensure that no one goes home hungry. Roast beef, assorted cold cuts, salami, turkey, chicken (available for a minimal extra cost) – our large meat trays can accommodate up to 35 people per tray, so you never have to worry about the size of your guest list!
For more information about the delectable meats available here, or to place a customized order for your next event, call us at Orillia Bakery & Deli at 705-326-5555.


Fig souring, or fig sour rot, is a nasty business that can render all the fruits on a fig tree inedible. It can be caused by a number of different yeasts and bacteria, but it is pretty much always spread by insects. Luckily, there are some easy and effective ways to avoid the problem. Keep reading to learn more about identifying sour figs and managing fig sour rot.
Symptoms of fig souring are usually easily recognizable. As the figs start to ripen, they will give off a fermented smell and a pink, syrupy liquid will begin to ooze from the eye, sometimes forming bubbles as it comes out.
Eventually, the flesh inside the fruit will liquefy and become covered in a white scum. The fruit will go limp and black, then shrivel up and either drop from the tree or remain there until it is removed.
The rot might then spread to where the stem attaches to the fruit, forming a canker in the bark.
Fig souring isn't a disease in and of itself, but rather the result of any of a large number of bacteria, fungi, and yeasts entering into the fig and essentially rotting it from within. These things enter the fig through its eye, or ostiole, the little hole at the base of the fruit that opens up as it ripens.
When this eye opens, tiny insects enter it and bring the bacteria with them. Nitidulid beetles and vinegar fruit flies are common insect culprits.
Unfortunately, once a fig has begun to sour, there's no saving it. Spraying insecticides to control the insects that spread bacteria is sometimes effective. The best way to prevent sour figs, however, is to plant varieties that have either narrow or no ostioles.
Some good varieties are Texas Everbearing, Celeste, and Alma.


text:
Review: Vigilante
October 10, 2011 Shannon
There was World War II, and then there was the Cold War. There was Vietnam, and Desert Storm, and the war on terror.
Now there's the war on crime. New York has fallen into the control of crime-lords, and cities throughout America are ravaged by thugs, big-time and small. The government is weak and hapless, and the people are afraid.
But The Hand is here to show them a better way. He protects innocents, stops criminals in the act, and answers 911 calls. He's like the police, only without paperwork and any regulations besides his own code.
Vigilante, Robin Parrish's latest book, is a unique blending of familiar flavors – a military thriller by way of a pulp hero with sci-fi gadgetry. The Hand is a masked hero, saving people from an endless line of villains and corrupt or bumbling government officials. But the hero is an ex-Special Forces soldier in a hooded sweatshirt, and the villains are criminals you could read about in the paper. Parrish does not create a new threat so much as he expands an old one until it overpowers society.
There's a good amount of violence. It's tastefully handled for the most part, though I could have done without at least one death. It wasn't gratuitous, just sad.
The novel does have a Christian strain, strong but not loud. Its protagonist is a Christian, and there is a tension in that; the Bible surely turns a cold gaze on vigilantism. Still, the characters never get around to debating the morality of going vigilante. But the book does.
I realized this as I reached the end. All that went on – the undeniable successes of The Hand, the good and bad he did, the temptations he fell to – all show the danger and the draw of a vigilante.
For all the action, the drama of Vigilante finds its heart in one man struggling for a city's soul, and then for his own. The story is well-told and interesting throughout; in some ways it is grim as Robin Parrish, with ultimate skill, faces that old and captivating figure – the Vigilante.
For the interested: Robin Parrish's website offers more on Vigilante, as does Amazon.
Book Reviews reviews, robin parrish, vigilante
Emancipating Minors, YA Style
Not Too Great a Good

redpajama_set_name: RedPajamaCommonCrawl


There are few words that come close to adequately describing the mark on our lives that Aretha Franklin has made. And fewer, still, that haven't already been shared by those who knew her best and loved her.
I'm not even going to try to surpass those statements. Instead, I'm simply going to talk for a few minutes.
When I saw the news posted earlier this week that Aretha was in hospice care at her home, I knew it was simply a matter of time. I was hoping that somehow she would be able to pull through this and get to spend some more time here among us. But when you hear and see the word "hospice" you know what is coming next.
I began to remember being a kid and my mother singing along with Aretha on the radio around the house or in the car. I remembered the feeling I got when I heard and listened to Aretha sing A Change Is Gonna Come.
And just like the first time I still get chills. At the time I was too young to understand the full depth of what the song was about. Having not lived that particular part of life, I'll likely never understand fully what fueled that passion and emotion she put into that piece. Being part of a minority of a different kind, I can relate in a way.
Consistently and steadily throughout my life, and I imagine most peoples lives in the US and abroad, her voice became a natural part of my world. Instantly recognizable and reassuring in the soundtrack of life.
Years later when I came more fully to the world of the Blues, I started to see the other side of the soul sound that Aretha put into her music. Not just the beautiful voice on the music charts, but the passion and meaning she put into every song singing from history and life.
In a genre that is uniquely American, the history that brought it forward informs a great deal the sound that it makes in our hearts. An honest and complete look at the good and the bad, the bright and the dark, the trouble and the peace.
When you hear the words and take the time to learn some of the history you can then begin to understand the lyrics to the songs in a much more meaningful way.
Aretha, having grown up a preachers daughter and starting in gospel, brought that passion and soul into her music naturally. It was the foundation upon which her future was built. And with that foundation she not only found a calling and a career, she found a way to help push forward the fight for equality in this country. She was able to celebrate the victories, the battles, and mourn the losses with that gift.
Something that I didn't learn until years later was that Aretha sang at Martin Luther King Jr's funeral. I learned that bit of history when I saw her sing at President Barack Obama's inauguration. Quite the book ends of the spectrum.
She could lift us all up and paint a picture of history and with her voice she could bring world leaders to tears.
Her final public performance being one of singing at a gala that Sir Elton John held as a benefit for AIDS research. Her willingness to stand in any lane to bring equality and peace forward is something that will forever mark her legacy.
Several years ago I was lucky enough to get the chance to see her perform live, just a few blocks from my home. Others have used similar words to describe hearing and seeing her perform and they are quite accurate... "Otherworldly" is the one that stands out for me because when I walked out of that concert I could only describe it as having felt like I'd been taken to the mountain top and shown what beauty can truly look and feel like.
Aretha Franklin has, in one way or another, touched all of our lives to some degree. If it was just having fun singing along in the car ride to the grocery store, enjoying a moment of equality amongst friends in a restaurant with there no longer being a "colored section", or in tears of joy and or pain when love has lifted us or left us.
Much of her efforts are obvious, and in a way that we may or may not ever truly understand, her efforts for beauty, art, music, equality, and kindness to one another have forever shaped the world in which we live.
The arts enrich our lives and keep society moving forward in times of darkness, they reach down and pull us up to propel us forward down the road. Without the arts the world would be a much different place and I would argue a much darker place. With artists like Aretha Franklin, B.B. King, Ray Charles, Etta James, and a list that continues into the decades of history; we have seen our world pushed forward and pulled together even when it seems to be ripping itself apart.
We lose celebrities frequently and we are saddened but move forward. When we lose icons and individuals who have moved more than concert ticket sales or election cycles, but instead moved us as a people forward in a positive manner, those are moments that will always live with us. Musicians, leaders, scientists, authors, and friends, when we lose those individuals then we are left with a mark on us of bittersweet memory.
Rest Aretha, you've carried us farther than you know and became a star. And now you rest among the stars that number the heavens and are a part of each and every single living thing in the world. From star dust comes life, and that life sometimes turns into a whole new kind of star.


There will be over 21 car classifications, with awards given for "Judges Favorite" in each class. Each judge will also have an "Ad Hoc" award to present to an additional car of their choice inside the class or to any car or their choice. There will be 5 or more dignitary awards. All special interest vehicles are welcome to participate in this event. T-shirts and dash plaques will be given to the first 100 participants who enter "show quality" judged vehicles. Under construction class will not be judged. Cars that do not meet show quality standards may be rejected. All cars must be able to be moved into and out of the park without a trailer. No "For Sale" signs. This event is intended for vehicle enthusiasts. Dealers are not invited. The show is free, no registration fee required for the Car Show. Fun Run is operated by the Bonnevills Rod & Custom Car Club. Fun Run hands are $5 each and include a barbeque at the last stop. Awards for 'Fun-Run" participants with the best hands will be provided at the awards ceremony to held Saturday (must be present). J.C. Hackett (Oldies Productions) will be on hand to provide music and entertainment for the show on Saturday.
All located with comfortable walking distance.
Saturday Parade: 2nd largest in Utah. Parade starts at 10:00 AM. Individual car owners or car clubs may make parade arrangements through the Box Elder Chamber of Commerce.
Commercial Booths: Browse through the many kinds of foods and drinks. Check out the trinkets and those long sought after treasures you've always wanted. Every year, it's a new and exciting adventure to wander through the many booths.
Carnival: Fun rides for the kids and the young at heart.
If you are a member of a Car/Motorcycle club enter it hear.
I agree to the Liability and Advertising Policy.
LIABILITY: In consideration of the acceptance of the right to participate, entrants, participants and spectators, by execution of their entry form release and discharge Box Elder Chamber of Commerce (BECofC), Brigham City, State of Utah, their officers, directors, employees, agents, representatives, and anyone else connected with management or presentation of the Peach Days Car Show and Fun run of and from any and all unknown damages, injuries, losses, judgments, and/or claims from any cause whatsoever that may be suffered by an entrant to his/her person or property. Further, each entrant expressly agrees to indemnify all forgoing entities, firms, persons, and bodies of and from any and all liability occasioned or resulting from the conduct of entrants or any participant assisting or cooperating with entrant and under the direction or control of entrant. All entrants and participants will act with respect and responsibility to other person and property, and maintain a safe speed 5 (five) MPH while on event property. ADVERTISING RELEASE: In consideration of this entry, car owner and family or guests agree to permit BECofC the use of their names and pictures of the car for publicity, advertising, and commercial purposes (including newspapers, magazines, radio, and television) before, during, and after the event and do hereby relinquish any rights whatsoever to any photos taken in conjunction with the event and give permission to BECofC. I have read and agree to all conditions of the Peach Days Car Show and rules governing the event as set forth on this form and agree to observe all rules and dictations of the event coordinators and/or managers.


These tiny terrors aggravate people and their pets with their itchy, painful bites. Fleas account for more than half of the dermatological conditions requiring veterinary assistance and even a single flea bite can be intensely irritating. Bites tend to be concentrated on the lower legs but can also occur elsewhere. Some bite victims also suffer from allergic dermatitis characterized by intense itching, hair loss, reddening of the skin and secondary infection. Cat fleas, the most common type of flea found in our area, may also be an intermediary host of dog tapeworms to animals and murine typhus to humans.
Adult fleas are small, dark-colored, wingless insects that are flattened from side to side. The most commonly encountered flea is the cat flea or Ctenocephalides felix. It has an incredibly tough body and it is difficult to kill a flea just by squeezing between the fingers.
Adult fleas feed exclusively on the blood of animals and people.
Flea larvae like to hide in dark areas close to the ground, like carpets. Eggs are commonly found on or near pet's resting areas. Adult fleas prefer to live on the host animal.
In addition to their annoying, itchy bites, fleas also spread diseases by serving as a vector for disease producing organisms. Fleas transmit diseases to both people and pets such as dermatitis, murine typhus, plague and tapeworm.


text:
\section{Introduction}

Despite the great success of deep neural networks for representation learning~\cite{He2015, he2019moco}, it heavily relies on collecting large-scale training data samples, which turns out to be non-trivial in real-world applications. Therefore, how to form robust deep representation learning under the data scarcity by exploring the sample relationships has received a lot of attention from the community, especially for the tasks without good training data to guarantee the generalization, such as long-tailed recognition~\cite{wang2020long}, zero-shot learning~\cite{naeem2021learning}, and domain generalization~\cite{cha2021swad}. However, it still remains a great challenge to find a unified, flexible, and powerful way to explore the sample relationships for robust representation learning. An intuitive example revealing the effectiveness of sample relationships is shown in Figure~\ref{fig:illu}.

Among recent data scarcity learning methods, sample relationships have been intensively explored using an explicit scheme from either regularization~\cite{shankar2018generalizing, zhang2018mixup, li2021metasaug,ghiasi2021simple} or knowledge transfer~\cite{wang2017learning, wang2021rsg,zhu2020inflated}. Specifically, a simple yet very effective way is to directly generate new data samples/domains from existing training data~\cite{li2021metasaug}, such as mixup~\cite{zhang2018mixup},  copy-paste~\cite{ghiasi2021simple}, crossgrad~\cite{shankar2018generalizing}, and compositional learning~\cite{kato2018compositional, hou2020visual, atzmon2020causal, naeem2021learning}. Another way is to transfer knowledge between data samples, e.g., 1) transferring meta knowledge between head and tail classes for long-tailed recognition~\cite{wang2017learning, liu2019openlongtailrecognition}; 2) transferring the knowledge from seen classes to unseen classes for zero-shot learning~\cite{xian2018zero,naeem2021learning}; and 3)
transferring invariant knowledge for domain generalization~\cite{peng2019domain, arjovsky2019invariant, huang2020self}. However, the above-mentioned methods explore sample relationships from either the input or output of deep neural networks, failing to enable deep neural networks themselves with the ability to explore sample relationships, i.e., there is no interaction from the view of batch dimension.

Enabling the learning on the batch dimension is not easy for deep neural networks due to the training and inference gap, i.e., we do not always have a mini-batch of data samples during testing. For example, batch normalization requires to always keep mini-batch training statistics, where the running mean and variance are then used to normalize testing samples~\cite{ioffe2015batch}. Another example uses the feature memory to keep category centers during training, which is then used to enhance the tail/unseen categories during testing~\cite{liu2019openlongtailrecognition, zhu2020inflated}. Therefore, to explore sample relationships for robust representation learning, we propose to empower the deep neural networks with structural advances for sample relationship learning. Specifically, we try to capture and model the sample relationships in each mini-batch of training data samples by introducing a transformer into the batch dimension, and we refer to it as the Batch Transformer or BatchFormer. Furthermore, to mitigate the gap between training and testing, we utilize a shared classifier before and after the BatchFormer module to enforce the batch-invariant learning. By doing this, the BatchFormer module is only required during training, i.e., we do not need to change the inference structures of deep neural networks.

From the perspective of optimization, BatchFormer enables the information propagation of all features of the mini-batch samples. Therefore, all samples can contribute to the learning on any object categories, and the insight might be that this implicitly enriches current training samples with hallucinated features from the whole mini-batch (\eg, the shared parts between two categories). For example, in long-tailed recognition, the hallucinated features may improve the feature space of the tail classes. Meanwhile, the loss function also emphasizes on the rare classes via propagating larger gradients of rare classes on other features in the mini-batch. In particular, we also find that BatchFormer brings two obvious changes to the learned representation, \ie, it effectively facilitates the deep model to learn 1) comprehensive representations by focusing on almost all different parts of the object; and 2) invariant representations by focusing on the object itself rather than the complex background cues (See more empirical evaluations in appendix).

In this paper, our main contributions can be summarized as follow: 1) we propose to explore sample relationships from the perspective of the internal structure of deep neural networks; 2) we devise a simple yet effective module termed as BatchFormer, which is a plug-and-play module to explore sample relationships in each mini-batch; and 3) without any bells and whistles, we perform extensive experiments to demonstrate the effectiveness of BatchFormer in a variety of visual recognition tasks, including long-tailed recognition, zero-shot learning, domain generalization, and self-supervised representation learning.

\begin{figure*}[!ht]
    \centering
    \includegraphics[width=0.9\linewidth]{BT_illu.pdf}
    \caption{The main framework of representation learning with the proposed BatchFormer. Specifically, we apply a BatchFormer module between the feature extractor (\eg, ResNet) and the classifier layer to explore the samples relationships. Furthermore, with a shared classifier before and after the BatchFormer during training for batch-invariant learning, we can then remove BatchFormer during testing.}
    \label{fig:struct}
\end{figure*}

\section{Related Work}

{\bf Sample Relationship.}
There are diverse and firm relationships among different samples, which have been widely used via various types of strategies~\cite{zhang2018mixup, liu2018learning, hou2019cross, mondal2021mini}. Zhang \etal~\cite{zhang2018mixup} propose to regularize the network to favor simple linear behavior in-between training examples with mixup. Mixup~\cite{zhang2018mixup} merely considers the linear transformation between examples, while we investigate the relationship among examples in a non-linear way. The compositionality of samples has inspired massive approaches to improve the generalization of few-shot and zero-shot learning~\cite{tokmakov2019learning, he2021compas, hou2020visual, naeem2021learning}, where the shared parts/attributes among different samples have been explored via prior label relationship knowledge. There are also some approaches via investigating sample/class relationships to conduct transductive inference~\cite{liu2018learning, hou2019cross, liu2019openlongtailrecognition, mondal2021mini}, \eg, transductive few-shot classification~\cite{liu2018learning}, meta embedding~\cite{liu2019openlongtailrecognition, zhu2020inflated}. However, those approaches require to conduct inference with multiple samples (\eg, query set, or bank features). Meanwhile, massive domain generalization methods~\cite{peng2019domain, arjovsky2019invariant, huang2020self} aims to find casual/invariant representations across domains, which we think internally utilizes the relationship among samples of the same class but different domains. However, those techniques are diverse and complex. By contrast, we propose to investigate relationships for robust representation learning in a simple yet effective way, and thus benefit those challenging tasks simultaneously.

\textbf{Data Scarcity Learning}.
Learning with imperfect training data has turned out to be very challenging, which has been explored in a variety of data scarcity tasks. A very important problem is long-tailed recognition, where
the data from different classes usually exhibit a long-tailed distribution: a large portion of classes have very few instances. Current long-tailed approaches can be roughly categorized into distribution re-balancing methods (\eg, re-sampling~\cite{chawla2002smote, han2005borderline, he2009learning}, re-weighting~\cite{byrd2019effect, cui2019class, tan2020equalization,zhong2021improving, jamal2020rethinking}), ensemble of diverse branches/experts~\cite{zhou2020bbn, wang2020long, cai2021ace}, and knowledge transfer~\cite{wang2017learning, liu2019openlongtailrecognition, zhu2020inflated, he2021distilling, wang2021rsg}.
Many knowledge transfer approaches have been developed for long-tailed recognition via meta learning~\cite{wang2017learning}, memory features~\cite{liu2019openlongtailrecognition, zhu2020inflated}, and virtual data generation~\cite{he2021distilling, wang2021rsg, hou2021fcl}. Nevertheless, those works usually depend on complex memory bank~\cite{liu2019openlongtailrecognition, zhu2020inflated}, or complex learning strategies~\cite{wang2017learning} or additional distillation step~\cite{he2021distilling}.
Another data scarcity tasks include zero-shot learning and domain generalization~\cite{blanchard2011generalizing, zhou2021domain, wang2021generalizing}, which require to recognize new categories with unseen training data. Specifically, zero-shot learning aims to recognize unseen classes that do not training samples, while domain generalization targets at generalizing classes of seen domains to unseen domains. Current zero-shot learning approaches ~\cite{lampert2009learning, palatucci2009zero, xian2018zero, wang2019survey} usually transfer knowledge of seen classes to unseen classes via modern techniques (\eg, graph network~\cite{naeem2021learning}, data generalization~\cite{zhu2018generative}, and compositional learning~\cite{naeem2021learning, hou2020visual}) for unseen classes recognition. Recently, compositional zero-shot learning~\cite{kato2018compositional, hou2020visual, atzmon2020causal, naeem2021learning} have been widely explored in different tasks, we thus mainly evaluate the proposed BatchFormer on compositional zero-shot learning~\cite{naeem2021learning}. Domain generalization techniques usually include data augmentation~\cite{zhou2020learning, zhou2021domain}, meta learning~\cite{balaji2018metareg, zhao2021learning}, and disentangled/invariant representation learning~\cite{peng2019domain, arjovsky2019invariant, chattopadhyay2020learning, piratla2020efficient}.  BatchFormer facilitates invariant representation learning, and thus illustrates effectiveness on multiple datasets.

{\bf Contrastive Self-Supervised Learning.}
Contrastive Learning~\cite{hadsell2006dimensionality} has increasingly achieved great success in computer vision for self-supervised learning, \eg, \cite{oord2018representation}, \cite{hjelm2018learning}, \cite{he2019moco, chen2020mocov2, chen2021mocov3} and \cite{chen2020simple}. Contrastive Learning aims to learn representations that attract similar samples and dispel different samples, where the similar samples and different samples are known according to some priors. However, we implicitly mine the relationships with Transformer Encoder network. Specifically, we also demonstrates the effectiveness of BatchFormer in contrastive learning, \eg, MoCo-v2~\cite{chen2020mocov2} and MoCo-v3~\cite{chen2021mocov3}.

\section{Method}

In this section, we first provide an overview of the main deep representation learning framework with the proposed BatchFormer module. We then introduce the proposed BatchFormer module in detail, including the encoder and the shared classifier. Lastly, we discuss the insights behind the proposed BatchFormer from the view of gradient flow.

\subsection{Overview}

The relationships between different samples are various and complex, while previous approaches have explored sample relationships using a straightforward way, such as the joint manipulation of different input images and the knowledge transfer using meta embedding or loss functions. Among existing methods, sample relationships are usually required to be defined in an explicit scheme before they can be used for learning, thus failing to automatically learn sample relationships for representation learning.

We consider sample relationships from a learning perspective, i.e., we aim to enable deep neural networks themselves with the ability to learn sample relationships from each mini-batch sample during the end-to-end deep representation learning. The main deep representation learning framework with the proposed new module is shown in Figure~\ref{fig:struct}. Specifically, a backbone network is first used to learn representations for individual data samples, i.e., there is no interaction between different samples in each mini-batch. After this, a new module is introduced to model the relationships between different samples by using the cross-attention mechanism in transformer, and we thus referred to it as Batch Transformer or BatchFormer module. The output of BatchFormer is then used as the input of the final classifier. To fulfill the gap between training and testing, we also utilize an auxiliary classifier before the BatchFormer module, i.e., by sharing the weights between the final classifier and the auxiliary classifier, we are able to transfer the knowledge learned from sample relationships to the backbone and auxiliary classifier. Therefore, during testing we can remove the BatchFormer and directly use the auxiliary classifier for classification.

\begin{algorithm}[!ht]
  \algsetup{linenosize=\tiny}
  \scriptsize
\SetAlFnt{\tiny}
\SetAlCapFnt{\small}
\SetAlCapNameFnt{\small}
\SetAlgoLined
\PyCode{def BatchFormer(x, y, encoder, is\_training):} \\
\Indp
\PyComment{x: input features with the shape [N, C]} \\
\PyComment{encoder: TransformerEncoderLayer(C,4,C,0.5)} \\
\PyCode{if not is\_training:}\\
    \Indp
    return x, y \\
    \Indm
\PyCode{pre\_x = x} \\
\PyCode{x = encoder(x.unsqueeze(1)).squeeze(1)} \\
\PyCode{x = torch.cat([pre\_x, x], dim=0)} \\
\PyCode{y = torch.cat([y, y], dim=0)} \\
\PyCode{return x, y} \\
\Indm
\caption{Pytorch Code of BatchFormer.}
\label{alg}
\end{algorithm}

\subsection{BatchFormer}

In this subsection, we introduce the detailed structures of the proposed BatchFormer. Specifically, the proposed BatchFormer module utilizes a stack of multiple transformer encoder layers to model the relationships between different samples.

\textbf{Transformer Encoder}. The transformer encoder includes multihead self-attention (MSA) and MLP blocks. A Layernorm (LN) is used after each block. Let $X \in R^{N\times C}$ denote a sequence of input features, where $N$ is the length of the sequence and $C$ indicates the dimension of input features. We then have the output of the transformer encoder as follows,
\begin{align}
\label{eq:msa}
\hat{X}_{l} &= LN(MSA(X_{l-1}) + X_{l-1}),\\
 X_l &= LN(MLP(\hat{X}_l) + \hat{X}_{l}),
\end{align}
where $l$ indicates the index of layers in the transformer encoder. The multi-head attention layers have been widely used to model the relationships from channel and spatial dimensions~\cite{vaswani2017attention, dosovitskiy2020image, Hu_2018_CVPR}. Therefore, we argue that it can also be extended to explore the relationships in the batch dimension. As a result, different from typical usage of transformer layers, the input of BatchFormer will be first reshaped to enable the transformer layers working on the batch dimension of the input data. By doing this, the self-attention mechanism in transformer layers then becomes the cross-attention between different samples for BatchFormer.

\textbf{Shared Classifier}. Since we can not assume batch statistics for testing, such as sample relationships, there might be a gap between the features before and after the BatchFormer module. That is, we can not perform inference on new samples by directly removing the BatchFormer. Therefore, apart from the final classifier, we also introduce a new auxiliary classifier to not only learn from the final classifier but also keep consistent with the features before the BatchFormer. To achieves this, we simply share the parameters/weights between the auxiliary classifier and the final classifier. We refer to this simple yet effective strategy as ``shared classifier". With the proposed ``shared classifier", we can thus remove the BatchFormer module during testing, while still benefiting from the sample relationship learning using BatchFormer.

\begin{figure}[!ht]
    \centering
    \includegraphics[width=0.9\linewidth]{BT_grads.pdf}
    \caption{Gradient propagation with BatchFormer in a mini-batch. Dashed lines represent new gradient propagation among samples.}
    \label{fig:grads_illu}
\end{figure}

The proposed BatchFormer is a plug-and-play module for robust deep representation learning. During optimization, the proposed BatchFormer modules can be jointly optimized with other deep learning backbone networks (including both CNNs and Vision Transformers) in an end-to-end way. The proposed BatchFormer is also very easy to implement using typical deep learning softwares,. For example, we show how to implement BatchFormer with several lines of PyTorch code in Algorithm~\ref{alg}.

\subsection{BatchFormer: A Gradient View}

To better understand how the proposed BatchFormer helps representation learning by exploring sample relationships, we also provide an intuitive explanation from the perspective of gradient propagation for optimization. Intuitively, without BatchFormer, all losses only propagate gradients on the corresponding samples and categories, \ie, one-to-one, while there are gradients on other samples with BatchFormer (the dashed line ) as illustrated in Figure~\ref{fig:grads_illu}. Specifically, given samples $X (X=X_0, X_1, X_i,..., X_{N-1})$ and the corresponding losses $L_0, L_1, L_i, ..., L_{N-1}$ in the mini-batch, with BatchFormer, we then have
\begin{equation}
    \label{eq:bt_grads}
    \frac{\partial{L_i}}{\partial{X}} = \frac{\partial{L_i}}{\partial{X_i}} + \sum_{j\neq i}^{N-1}{\frac{\partial{L_i}}{\partial{X_j}}}.
\end{equation}
That is, BatchFormer brings new gradient terms $\frac{\partial{L_i}}{\partial{X_j}}$, where $i \neq j$. From a perspective of gradient optimization, $L_i$ also optimizes the network according to sample $X_j (j\neq i)$, that is a significant difference compared to the model without BatchFormer. In other word, $X_j (j\neq i)$ can be regarded as a virtual sample~\cite{zhang2018mixup, he2021distilling} of $y_i$, where $y_i$ is the label of $X_i$. We consider that both BatchFormer and Mixup~\cite{zhang2018mixup} can be regarded as data-dependent augmentations. BatchFormer implicitly draws virtual examples from the vicinity distribution of samples via cross-attention module. From this perspective, BatchFormer has implicitly augmented $N-1$ virtual samples for each label $y_i$ via the relationship modeling among samples in the mini-batch. Previous approaches~\cite{balaji2018metareg, zhu2018generative} have demonstrated data augmentation is helpful for long-tailed recognition~\cite{he2021distilling, wang2021rsg}, zero-shot learning~\cite{zhu2018generative}, and domain generalization~\cite{zhou2020learning, zhou2021domain}.
the virtual samples are largely helpful for tail classes since those classes lack of samples. Our gradients analysis in Section~\ref{sec:grad_analysis} also demonstrates the tail classes have larger gradients on other samples compared to head classes.

\section{Experiments}

In this section, we perform extensive experiments to demonstrate the effectiveness of BatchFormer for a variety of data scarcity learning tasks, including long-tailed recognition, zero-shot learning, domain generalization and contrastive learning. Appendix provides more results of other tasks, \eg, image classification (include Visual Transformer~\cite{touvron21adeit, dosovitskiy2020image}), and more ablation studies.

\subsection{Long-Tailed Recognition}

{\bf Datasets}. We use four popular datasets for long-tailed recognition as follows: 1) CIFAR-100-LT has 50,000 training images and 10,000 validation images with 100 categories.
2) ImageNet-LT~\cite{liu2019openlongtailrecognition} contains 115.8K images of 1000 classes from ImageNet 2012. The number of images in each class ranges from 5 to 1,280; 3) iNaturalist 2018~\cite{van2018inaturalist} is a large-scale fine-grained dataset with 437.5K images from 8,142 categories; 4) Places-LT~\cite{liu2019openlongtailrecognition} is a long-tailed scene classification dataset derived from the Places dataset~\cite{zhou2017places} with 184.5K images from 365 categories with class instances ranging from 5 to 4,980.

{\bf Baselines}.
We use the following three baseline methods: Balanced Softmax~\cite{ren2020balanced}, RIDE~\cite{wang2020long} and PaCo~\cite{cui2021parametric}. If not otherwise stated, we follow the same settings used in previous methods, and the proposed BatchFormer is removed during testing. Particularly, there is a small difference when comparing with RIDE~\cite{wang2020long}. we train the network with a batch size of 400 on 4 V100 GPUs for 100 epochs with an initial learning rate of 0.1 on ImageNet-LT and 0.2 on iNaturalist 2018, respectively. The learning rare is decayed with cosine schedule on iNaturalist 2018. We report the performance of BatchFormer based on 3 experts RIDE.
All experiments are conducted on V100 GPU with PyTorch. See other details in Appendix A.

\begin{table}[!tp]
\setlength\tabcolsep{2.5pt}
\small
\caption{Illustration of imbalance ratio 100 and 200 on CIFAR-100-LT. $\ast$ means we train the method with the released code in one stage (\eg, balanced softmax~\cite{ren2020balanced}). Med means Medium category. RIDE* means we use 3 experts.}
\label{table:cifar100_lt}
\centering

\begin{tabular}{@{}lcccc|cccc@{}}
\hline
 \multirow{2}{*}{Method} &
\multicolumn{4}{c}{100}&\multicolumn{4}{c}{200} \cr
 & All & Many & Med & Few  & All & Many & Med & Few \\
\hline

RIDE* ~\cite{wang2020long}& 48.0 & 68.1 & 49.2 & 23.9 & - & - & - & -\\
\hline
Balanced$\ast$ ~\cite{ren2020balanced} & 50.7 & 68.0 & 49.7 & 31.9 & 46.4 & 70.0     & 51.5   &  24.3  \\
+ BatchFormer & \bf{51.7} & \bf{68.4} & 49.3 & \bf{34.3} & {\bf 47.5} & {\bf 70.2}  &  {\bf 53.3}    & {\bf 25.5} \\
\hline
Paco~\cite{cui2021parametric} & 51.9 & 63.9 & {\bf 53.0} & {\bf 36.5} & 47.1 & 68.1 & 51.5 & 27.5\\
+ BatchFormer & {\bf 52.4} & {\bf 68.4} & 52.1 & 34.0 & {\bf 47.8} & 68.1 & {\bf 53.0} & {\bf 28.2} \\

\hline
\end{tabular}
\end{table}

\begin{table}[tp]
\small
\setlength\tabcolsep{2.5pt}
\caption{Illustration of ResNet-10 and ResNet-50 on ImageNet-LT. $\ast$ means we train the net work with the released code by one-stage. RIDE-3e means we use three experts in RIDE.}
\label{table:imagenet_lt}
\centering

\begin{tabular}{@{}lcccc|cccc@{}}
\hline
 \multirow{2}{*}{Method} &
\multicolumn{4}{c}{ResNet-10}&\multicolumn{4}{c}{ResNet-50} \cr
 & All & Many & Med & Few  & All & Many & Med & Few \\
\hline
OLTR \cite{liu2019openlongtailrecognition} & 35.6 & 43.2 & 35.1 & 18.5 & - & - & - & \\
LFME~\cite{xiang2020learning} & 38.8 & 47.0 & 37.9 & 19.2 & - & - & - & -\\

\hline
Balanced$\ast$ ~\cite{ren2020balanced} & 41.0 & 52.6 &   38.3 &  18.0 &  50.1 & 61.1  &   47.5   & 27.6    \\
+ BatchFormer\dag & {\bf 43.2} & {\bf 52.8}   &  {\bf 40.7}  &  {\bf 24.9}  & {\bf 51.1} & {\bf 61.4}   &  {\bf 47.8}  &  {\bf 33.6}       \\
\hline
RIDE-3e $\ast$ ~\cite{wang2020long} & 44.7 & 57.0 & 40.3 & 25.5 & 53.6 & 64.9 & 50.4 & 33.2 \\
+ BatchFormer & {\bf 45.7} & 56.3 & {\bf 42.1} & {\bf 28.3} & {\bf 54.1} & 64.3 & {\bf 51.4} & {\bf 35.1}\\
\hline
PaCo~\cite{cui2021parametric} & - & - & - & - & 57.0 & 64.8 & 55.9 & 39.1 \\
+ BatchFormer & - & - & - & - & {\bf 57.4} & 62.7 & {\bf 56.7} & {\bf 42.1}\\
\hline\hline
two stage \\
\hline
RIDE-3e ~\cite{wang2020long} & 45.9 & 57.6 & 41.7 & 28.0 & 54.9 & 66.2 & 51.7 & 34.9 \\
+ BatchFormer & {\bf 47.6} &  55.3 & {\bf 45.5} & {\bf 33.3} & {\bf 55.7} & 64.6 & {\bf 53.4} & {\bf 39.0} \\
\hline
\end{tabular}
\end{table}

{\bf Results on CIFAR-100-LT}. Table~\ref{table:cifar100_lt} demonstrates the proposed BatchFormer module is orthogonal to the state-of-the-art methods, \eg, Balanced Softmax and Paco. We notice that BatchFormer largely improves Balanced Softmax by 2.4\% for few classes when imbalance ratio is 100, and by 1.8\% on medium classes and by 1.2\% on few classes respectively when imbalance ratio is 200. Besides, the results of PaCo on imbalance ratio 200 also increase by 1.5\% on medium classes and 0.7\% on few classes with BatchFormer. For ratio 100, BatchFormer mainly improves many classes since Paco has achieved good performance on few classes. Please refer to Appendix for more explanations (\ie, the comparison without PaCo loss, but with strong data augmentation). Overall, BatchFormer improves the recognition of tail classes while maintaining the performance of head classes. Meanwhile BatchFormer is pluggable to current popular methods on CIFAR-100-LT.

{\bf  Results on ImageNet-LT}. As illustrated in Table~\ref{table:imagenet_lt}, BatchFormer largely improves Balanced Softmax by 2.4\% on medium classes and {\bf 6.9\%} on few classes respectively under ResNet-10 backbone. Meanwhile, under ResNet-50 backbone, BatchFormer largely improves Balanced Softmax on the Few category by {\bf 5\%}.

When BatchFormer is applied in RIDE~\cite{wang2020long}, the results on medium classes and few classes increase by 1.8\% and 2.8\% respectively under ResNet-10 backbone. BatchFormer also increases medium and few classes by 1\% and 1.9\% respectively under ResNet-50. BatchFormer achieves clear improvement overall, while the performance on many classes drops a bit. Furthermore, BatchFormer also effectively improves RIDE under two-stage training strategy (RIDE uses a larger model to teach small model with distilling loss). Here, different from RIDE, we use a pre-trained model (the same model) to initialize the model and train the model again with BatchFormer (See details in Appendix).

PaCo~\cite{cui2021parametric} is recently introduced for Long-Tailed Recognition with Supervised Contrastive Learning. We also find BatchFormer is able to facilitate ImageNet-LT on medium classes and few classes. Noticeably, PaCo uses a strong data augmentation strategy from Supervised Contrastive Learning with 400 training epochs. We think data augmentation limits the improvement of BatchFormer on ImageNet-LT. Particularly, Appendix also shows BatchFormer achieves comparable results on balanced Full ImageNet.

\begin{table}[tp]
\small
\caption{Illustration on iNaturalist 2018. $\ast$ means we train the method with the released code in one stage. RIDE-3e means RIDE with 3 experts.}
\label{table:inaturalist}
\centering

\begin{tabular}{@{}lcccc@{}}
\hline
Methods & All & Many & Medium & Few \\
\hline
BBN~\cite{zhou2020bbn} & 66.3 & 49.4 & 70.8 & 65.3 \\
cRT~\cite{kang2019decoupling} & 65.2 & 69.0 & 66.0 & 63.2 \\
RIDE-3e~\cite{wang2020long} & 72.2 & 70.2 & 72.2 & 72.7 \\
PaCo~\cite{cui2021parametric} & 73.2 & - & - & -\\
\hline
RIDE-3e$\ast$~\cite{wang2020long}  & 72.5 & 68.1 & 72.7 & 73.2 \\
+ Batchformer & {\bf 74.1} & 65.5 & {\bf 74.5} & {\bf 75.8} \\

\hline
\end{tabular}
\end{table}

{\bf  Results on iNaturalist 2018}. We mainly evaluate BatchFormer on RIDE since Balanced Softmax has limited performance on iNaturalist 2018 and PaCo requires over 36 GPU days to converge (See Appendix). We train RIDE with cosine decay learning-rate scheduler by one-stage and achieve a strong baseline with 72.5\% (better than the reported) as illustrated in Table~\ref{table:inaturalist}. Particularly, BatchFormer further improves the Medium category and Few Category by 1.8\% and 2.6\% respectively. Here, for a fair comparison to previous work, we mainly evaluate BatchFormer RIDE with 3 experts which has similar GFlops to previous work.

\begin{table}[tp]
\small
\caption{Illustration of Places-LT (Backbone is ResNet-152).  $\ast$ means the result that we train the method with the released code.}
\label{table:places_lt}
\centering

\begin{tabular}{@{}lcccc@{}}
\hline
Methods & All & Many & Medium & Few \\
\hline

OLTR~\cite{liu2019openlongtailrecognition} & 35.9 & 44.7 & 37.0 & 25.3 \\
$\tau$-normalized~\cite{kang2019decoupling} & 37.9 & 37.8 & 40.7 & 31.8 \\
\hline
BALMS*~\cite{ren2020balanced} & 37.8 & 41.4 &	   38.8 &	 29.1 \\
+ Batchformer & {\bf 38.2} & 39.5  &   38.3  &  {\bf 35.7} \\
\hline
PaCo~\cite{cui2021parametric} &  41.2 & 37.5 & {\bf 47.2} & 33.9 \\
+ Batchformer &  {\bf 41.6} & {\bf 44.0} & 43.1 & 33.7 \\
\hline
\end{tabular}
\end{table}

{\bf  Results on Places-LT}. Table~\ref{table:places_lt} illustrates BatchFormer improves BALMS on Few category. Besides, BatchFormer effectively improves PaCo~\cite{cui2021parametric} which is the new state-of-the-art on Places-LT. Here, different from CIFAR-100-LT and ImageNet-LT, BatchFormer mainly improves Many category. This might be because the result of PaCo on Many category is very worse and BatchFormer can re-balance the imbalanced datasets.

\subsection{Zero-Shot Learning}
We evaluate BatchFormer on compositional zero-shot learning~\cite{naeem2021learning}. All experiments are evaluated with one V100 GPU. The learning rare, epochs, optimizer are fully similar to ~\cite{naeem2021learning}. See details in Appendix A.

{\bf Datasets.} The experiments are performed on three datasets: 1) MIT-States~\cite{isola2015discovering} consists of 30,000 training images of natural objects with 1,262 seen compositions (23.8 image per composition, 115 states and 245 objects), and 13,000 test images with 400 seen compositions and 400 unseen compositions; 2) UT-Zappos~\cite{yu2014fine} includes 23,000 training images of shoes catalogue and we use the splits from~\cite{purushwalkam2019task}. UT-Zappos has 83 seen compositions for training (277.1 images per composition, 16 states, 12 objects) and 18 seen compositions and unseen compositions in test set; 3) C-GQA~\cite{naeem2021learning} provides 26,000 training images with 6,963 seen compositions (3.7 images per composition, 453 states, 870 objects) and 3,000 test images with 18 seen compositions and unseen compositions.

{\bf Metrics.} We adopt the evaluation protocol of ~\cite{purushwalkam2019task} and report the Area Under the Curve (AUC) (in \%) between the accuracy on seen and unseen compositions. Similar to~\cite{naeem2021learning}, we also report unseen accuracy and seen accuracy, as well as the best harmonic mean.

Table~\ref{table:illu_zs} demonstrates BatchFormer effectively improves the AUC and HM among all the datasets compared to the baseline. Particularly, for a fair comparison, we use the released code under the same setting to reproduce ~\cite{naeem2021learning} as the baseline. We notice BatchFormer mainly improves the Seen category on MIT-States and C-GQA, while BatchFormer largely improves the unseen category by nearly {\bf 5\%}. This might be because the number of seen composition instances on MIT-States and C-GQA is few, \eg 23.8 image per seen composition on MIT-States and 3.7 images per seen composition. In other words, the recognition of seen compositions on MIT-States and C-GQA is few-shot learning. We think BatchFormer on the two datasets mainly finds invariant features among images of the same class.

\begin{table*}[tp]
\setlength\tabcolsep{4pt}
\small
\caption{Illustration of Batchformer on Compositional Zero-Shot Learning. * means we use the released code to reproduce the results. S means seen, U means unseen, s means state and o means object. The results of ~\cite{li2020symmetry} are copied from ~\cite{naeem2021learning}. }
\label{table:illu_zs}
\centering

\begin{tabular}{@{}lcccccc|cccccc|cccccc@{}}
\hline
 \multirow{2}{*}{Method} &
\multicolumn{6}{c}{MIT-States}&\multicolumn{6}{c}{UT-Zap50K} &\multicolumn{6}{c}{C-GQA} \cr
 &  AUC & HM & S & U & s & o & AUC & HM & S & U  & s & o & AUC & HM & S & U  & s & o\\
\hline
CompCos~\cite{mancini2021open} & 4.5 & 16.4 & 25.3 & 24.6 & 27.9 & 31.8 & 28.7 & 43.1 & 59.8 & 62.5&44.7 & 73.5&  - & - & -& -& -& -\\

CGE~\cite{naeem2021learning} & 6.5 & 21.4 & 32.8 & 28.0& 30.1& 34.7& 33.5 &60.5& 64.5& 71.5& 48.7& 76.2 &3.6& 14.5& 31.4& 14.0& 15.2& 30.4 \\
\hline
CGE* & 6.3 & 20.0 & 31.6 & 27.3 & 30.3 & 34.5 & 31.5 & 46.5 & 60.3 & 64.5 & 46.3 & 74.4 & 3.7 & 14.9 & 30.8 & 14.7 & {\bf 15.8} & 29.0 \\
+BatchFormer & {\bf 6.7} & {\bf 20.6} & {\bf 33.2} & {\bf 27.7} & {\bf 30.8} & {\bf 34.7} & {\bf 34.6} & {\bf 49.0}  & {\bf 62.5 }& {\bf 69.2 }& {\bf 49.7} & {\bf 75.6} & {\bf 3.8} & {\bf 15.5} & {\bf 31.3} & 14.7 & 15.3 & {\bf 30.0} \\

\hline
\end{tabular}
\end{table*}

\subsection{Domain Generalization}

By exploring sample relationships of the same class, it is easier to find the invariant features and thus improve the domain generalization. We first demonstrate BatchFormer effectively improves the baseline (without domain generalization techniques) on PACS~\cite{li2017deeper} under ResNet-18. Then, we apply BatchFormer to recent domain generalization, \eg, SWAD~\cite{cha2021swad}, and shows the effectiveness of BatchFormer.

{\bf Datasets}. We illustrates the application of BatchFormer on several domain generalization datasets: 1) PACS~\cite{li2017deeper} covers 7 object categories and 4 domains (Photo, Art Paintings, Cartoon and Sketches), 2) VLCS~\cite{fang2013unbiased} (4 domains, 5 classes, 10,729 images), OfficeHome~\cite{venkateswara2017deep} (4 domains, 65 classes and 15,588 images), and TerraIncognita~\cite{beery2018recognition} (4 domains, 10 classes and 24,788 images).

{\bf Details}. For baseline, we train the network under ResNet-18 with SGD (30 epochs, initial learning rate 0.001), and drop the learning rate at 24 epochs. We also use the popular data augmentations, \eg, flip, color jiter and scale. We train the network 5 times and report the average. Others methods are compared based on ~\cite{dalib}. For a fair comparison with SWAD~\cite{cha2021swad}, we follow the optimization methods of~\cite{cha2021swad} and use the released code of ~\cite{cha2021swad} to evaluate BatchFormer on ResNet-18. See more details in Appendix.

Table~\ref{table:dg_pacs} illustrates BatchFormer consistently improves baseline , CORAL~\cite{deep_coral} and MixStyle~\cite{zhou2021mixstyle}. BatchFormer also clearly improves recent work~\cite{cha2021swad} on four datasets in Table~\ref{table:dg_swad}. Particularly, BatchFormer improves~\cite{cha2021swad} by over 2\% on OfficeHome and TerraIncognita. This illustrates BatchFormer is able to facilitate invariant representation learning, and improve the generalization across domains.

\begin{table}[tp]
\setlength\tabcolsep{2.5pt}
\small
\caption{Illustration of BatchFormer for Domain Generalization under different works on PACS~\cite{li2017deeper}.}
\label{table:dg_pacs}
\centering
\begin{tabular}{@{}lccccc@{}}
\hline
Methods  & art\_paint & cartoon & sketches & photo & Avg.  \\
\hline
Baseline &79.9$\pm$ 1.0 & 73.0$\pm$1.5 & 67.7$\pm$ 3.0& 95.7$\pm$0.4 & 79.1\\
+BatchFormer & {\bf 80.4}$\pm$0.2 & {\bf 73.8}$\pm$2.0& {\bf 68.6}$\pm$1.8&{\bf 96.3}$\pm$0.2 & {\bf 79.8}\\
\hline
CORAL~\cite{deep_coral} & 79.2$\pm$1.7 &  {\bf 75.5} $\pm$1.1 & 71.4$\pm$3.1 & 94.7$\pm$0.3 &  80.2 \\
+BatchFormer & {\bf 80.6}$\pm$0.9 &  74.7$\pm$1.9 & {\bf 73.1}$\pm$0.3& {\bf 95.1}$\pm$0.3 & {\bf 80.9} \\
\hline
MixStyle~\cite{zhou2021mixstyle} & 81.7$\pm$0.1 &  {\bf 76.8}$\pm$0.0 & 80.8$\pm$0.0 &  93.1$\pm$0.0 & 83.1 \\
+BatchFormer & {\bf 84.8} $\pm$0.4 &  75.3$\pm$0.0 &  {\bf 81.1} $\pm$0.4 & {\bf 93.6}$\pm$0.0 &  {\bf 83.7}\\

\hline
\end{tabular}
\end{table}

\begin{table}[tp]
\small
\caption{Illustration of BatchFormer for Domain Generalization based on recent work~\cite{cha2021swad} (ResNet-18). We show the average results of different domains. Terra is TerraIncognita. See more results in Appendix. }
\label{table:dg_swad}
\centering
\begin{tabular}{@{}lcccc@{}}
\hline
Methods  & PACS & VLCS & OfficeHome & Terra   \\
\hline
SWAD*~\cite{cha2021swad} & 82.9 & 76.3& 62.1 & 42.1 \\
 + BatchFormer & {\bf 83.7} & {\bf 76.9} & {\bf 64.3} & {\bf 44.8}\\
\hline
\end{tabular}
\end{table}

\begin{table}[tp]
\small
\caption{Illustration of BatchFormer for Contrastive Learning (MoCo~\cite{he2019moco, chen2020mocov2, chen2021mocov3} under ResNet50) on linear  classification.}
\label{table:moco}
\centering
\begin{tabular}{@{}lccc@{}}
\hline
Methods & Epochs & Top-1 & Top-5 \\
\hline
MoCo-v2~\cite{chen2020mocov2}    & 200 & 67.5 & -\\
MoCo-v2~\cite{chen2020mocov2}+BatchFormer & 200 & {\bf 68.4} & 88.5\\
\hline
MoCo-v3~\cite{chen2021mocov3} & 100 & 68.9 & - \\
MoCo-v3~\cite{chen2021mocov3}+BatchFormer & 100 & {\bf 69.8} & 89.5 \\
\hline
\end{tabular}
\end{table}

\begin{figure}
    \centering
    \includegraphics[width=.4\textwidth]{moco_converge_speed.pdf}
    \caption{Convergence of BatchFormer under linear classification. The batch size and optimizer are the same as~\cite{chen2021mocov3}.}
    \label{fig:conveg_bt}
\end{figure}

\subsection{Self-Supervised Learning}
Contrastive Learning aims to learn representations that attract similar samples and dispel different samples, while BatchFormer builds a Transformer Network among samples to implicitly explore the sample relationships for representation learning. BatchFormer can also be applied to Contrastive Learning. We mainly evaluate BatchFormer with MoCo-v2~\cite{chen2020mocov2} and MoCo-v3~\cite{chen2021mocov3} on linear classification protocol. Object detection result is provided in Appendix.

Table~\ref{table:moco} shows BatchFormer is also able to improve the representation learning of self-supervised learning, \eg, BatchFormer consistently improves MoCo-v2 and MoCo-v3 by around 1.\% on ImageNet. Figure~\ref{fig:conveg_bt} shows the linear classification of BatchFormer pre-trained model convergences faster, \eg, BatchFormer pre-trained model achieves the performance of moco-v3 with only 38 epochs.

\subsection{Ablation Studies}
\label{sec:ab}

\begin{figure*}
    \centering
    \includegraphics[width=.8\textwidth]{BT_cam_illu_tworow.pdf}

    \caption{Grad-Cam demonstration of BatchFormer on low-shot test images based on~\cite{ren2020balanced}. The first row is baseline, while the second row is BatchFormer. The left images show BatchFormer enables the model pay attention on more details when the scene is simple and clean, while the right images show BatchFormer faciliates the model ignore the spurious correlation in the image. More figures are in Appendix.}
    \label{fig:cam_illu}
\end{figure*}

\begin{figure}
    \centering
    \includegraphics[width=.4\textwidth]{grads_test_per_cls.pdf}
    \caption{The gradient of each class to other images in mini-batch on CIFAR-100-LT and ImageNet-LT (based on~\cite{ren2020balanced}). For each class, we obtain the gradient normalization of the loss of each label to other images in the mini-batches, and then average the gradients of each class in the test set. The classes are sorted by descending order according to the number of instances in the training set.}
    \label{fig:grad}
\end{figure}

{\bf Batch size.} The batch size represents the size of Transformer. Table~\ref{table:ab_batch_size} shows BatchFormer is less sensitive to the batch size when batch size is less than 128. We find batch size 512 achieves better performance on Few category.

\begin{table}[tp]
\small
\caption{Ablation Studies of Batch Size based on Balanced Softmax~\cite{ren2020balanced}. The backbone is ResNet-10 under one GPU. learning rare is linearly decreased with batch size.}
\label{table:ab_batch_size}
\centering
\begin{tabular}{@{}lcccccc@{}}
\hline
Batch Size & 16 & 32 & 64 & 128 & 256 & 512  \\

\hline
All &  43.2  & {\bf 43.6} & 43.3 & 43.2 & 42.4 &42.5\\
Many & {\bf 53.8} & 53.7 & 52.9&   52.8 & 52.2 & 52.0\\
Medium & 40.2 & 40.7 & {\bf 40.8} &   40.7 &    39.5 & 39.6\\
Few & 23.9  &24.9 &  25.3 &  24.9 &  25.2 & {\bf 25.8 }\\
\hline
\end{tabular}
\end{table}

\begin{table}[tp]
\small
\caption{Ablation Studies on ImageNet-LT based on Balanced Softmax~\cite{ren2020balanced}. The backbone is ResNet-50.}
\label{table:ab_modules}
\centering

\begin{tabular}{@{}lcccc@{}}
\hline
Method & All & Many & Medium & Few \\
\hline
BatchFormer & {\bf 50.9} & {\bf 60.7} & {\bf 47.7} & 34.1 \\
w/o Shared Classifier &42.4 & 41.3  & 43.3 & {\bf 42.2} \\
\hline
\end{tabular}
\end{table}

{\bf Shared Classifier}. Interestingly, Table~\ref{table:ab_modules} demonstrates BatchFormer without shared classifier achieves similar performance on three categories, while BatchFormer with shared classifier maintains the performance on Many category. This demonstrates the effectiveness of shared classifier and the ability of re-balancing of BatchFormer.

\section{Visualization Analysis}
\label{sec:visualize}
\label{sec:grad_analysis}

{\bf Visualized Comparison}.
We illustrate the visualized comparison with Grad-CAM~\cite{selvaraju2017grad} between Baseline and BatchFormer in Figure~\ref{fig:cam_illu}. We find BatchFormer focuses on more details of objects and ignores spurious correlations.
On the one hand, when the image includes complex scence with many disturbing factors, BatchFormer effectively improves the attention of the network on the corresponding object regions (\eg, sea snake on the sandbeach, dog on the snow and insect on the leaf in Figure~\ref{fig:cam_illu} right). On the other hand, BatchFormer also pays more attention on regions of the object when the scene is clear (\eg, the bird, dog, and spider in Figure~\ref{fig:cam_illu} left). See more examples in Appendix.

{\bf Gradients Analysis}.
BatchFormer has increased new gradient backward: the loss of each label has the gradients on other images. In other words, we have implicitly augmented the samples for the class of each image in the mini-batches. The other images in the mini-batch can also be regarded as the virtual instances of current image class.  The gradient is firmly related to the effect of each image label on other images. Figure~\ref{fig:grad} illustrates the rarer the class is, the larger the gradients of the class have on other images in the mini-batch. Thus, BatchFormer actually utilizes the other images to facilitate low-shot recognition via increasing gradients of few-shot labels on other images.

\section{Conclusion and Future work}

We propose to enable deep neural networks themselves with the ability to explore the sample relationships from each mini-batch. Specifically, we regards each image (batch  dimension) in the mini-batch as a node of a sequence, and then build a Transformer Encoder Network among the images to mine the relationships among the images in the mini-batch. BatchFormer enables the gradient propagation of each label to all images in the mini-batch, which can be regarded as virtual sample augmentation, and thus improve the representation learning. We further introduce a shared classifier before and after the BatchFormer during training,  which can thus be removed during testing.  We demonstrate the effectiveness of BatchFormer on over ten datasets and BatchFormer achieves significant improvements on different tasks, including long-tailed recognition, zero-shot learning, domain generalization, and contrastive learning. \\
{\small
{\bf Limitations} The improvement of current BatchFormer on models with strong data augmentation and balanced distribution is limited. \\
{\bf Acknowledgements} {\small Dr. Baosheng Yu and Mr. Zhi Hou are supported by ARC FL-170100117.}}

{\small
\bibliographystyle{ieee_fullname}

redpajama_set_name: RedPajamaArXiv


text:
Kino Lorber, Arthouse Group, Buys International TV Streamer MHz Choice – The Hollywood Reporter
November 3, 2022 by Aishwarya Kumari
New York-based arthouse distributor Kino Lorber has acquired MHz Networks, the parent company of MHz Choice, an online streaming service dedicated to international television series.
Under the deal announced Thursday, Kino Lorber will add its library of more than 5,000 titles to MHz Choice, which specializes in non-US TV series, streaming shows such as Scandinavian crime dramas. Wallander And Beckor the procedure of the French period Paris Police 1900. The entire staff of both MHz Networks and Kino Lorber will remain in place, with CEO Frederick Thomas and SVP of Content Strategy Lance Schwalst leading MHz Networks within the Kino Lorber Media Group.
"MHz's track record of curating best-in-class series from around the world has helped it build the most loyal subscriber audience in streaming," Kino Lorber President and CEO Richard Lorber said in a statement. "At a time when Hollywood mega-streamers are going bigger and wider, we like the idea of going deeper into niche content to super-serve a more discerning audience."
MHz Networks, which began as an extension of American Public Broadcasting, launched its SVOD service MHz Choice in 2015. Kino Lorber is one of the most prolific distributors in the arthouse space, with a recent slate that includes features such as Maggie Peren's WW2 drama. The ForgerIt premiered in Berlin, Cannes headlines 1976 Manuela Martelli and are bad From the 2019 Venice Film Festival entry by directors Lise Acocca and Roman Guerrette and Pietro Marcello Martin EdenIt acted The Old Guard Actor Luca Marinelli.
In 2019, Kino Lorber Company first expanded into digital distribution with the launch of its TVOD service Kino Now. During COVID, Kino Lorber launched a "virtual cinema" offering, Kino Marquee. And last year, the company launched its first ad-supported streaming service, Kino Cult.


text:
Is water safe to drink in Iceland?
Yes, the water is perfectly safe to drink in Iceland! Unless marked otherwise, tap water is considered safe, and because of where it comes from, you know you're getting it in its purest form. Nearly all of Iceland's tap water is sourced from groundwater – stored deep in wells under the earth's surface. Icelandic water is free of chlorine, calcium, and nitrate, and undergoes strict monitoring, clearing it of microorganisms and other pollutants. If you are dispensing hot water from the tap, you may notice a faint sulphur smell as most hot water in Iceland is heated geothermally. To avoid this, it is best to boil cold water from the tap if you wish to make a hot drink.
For environmental reasons, try to avoid buying bottled water. Bring a reusable bottle or canteen (we recommend at least a 1.5 litre capacity) that can be refilled as needed.
Click to read about what to drink in Iceland
Click to return to Iceland FAQs
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Home NMSU Cooperative Extension Service and Agricultural Experiment Station Publications Page 1
Opportunities to produce Canola in northern New Mexico: Results of field variety trials
101449.pdfpage
1Respectively, Professor (retired), Department of Plant and Environmental Sciences (PES); Professor, Department of Agricultural Economics and Agricultural Business (AEAB); Professor, AEAB; Graduate Student, AEAB; and Assistant Professor, PES, New Mexico State University.
COLLEGE OF AGRICULTURAL, CONSUMER AND ENVIRONMENTAL SCIENCES
aces.nmsu.edu/pubs • Agricultural Experiment Station • Research Report 793
Opportunities to Produce Canola
in Northern New Mexico:
Results of Field Variety Trials
Michael K. O'Neill, Jay Lillywhite, Gerald Hawkes, Mario Trillanes, and Koffi Djaman1
aces.nmsu.edu
The College of
Consumer and
Sciences is an
engine for economic and community
development in New Mexico, improving the lives of New Mexicans through academic, research, and Extension
programs.
Canola (Brassica
napus; Figure 1), introduced by Canadian agronomists in the 1970s, has become an important agronomic crop for several states in the U.S. Producers from traditionally non-canola-producing regions are exploring opportunities to incorporate canola into their crop rotations. This study examined potential returns from producing canola in semi-arid northern New Mexico at the New Mexico State University Agricultural Science Center at Farmington (ASCF). The study used six-year variety trial results from plots managed by ASCF researchers compared to results from the National Winter Canola Varity Trial (NWCVT) over the same six-year period. Canola crop cost and return budgets were compared with other crops grown in the region. The greatest canola grain yields at ASCF were 4,150 lb/acre during the 2008/09 season and 4,321 lb/acre during the 2011/12 season. Over the length of the test, canola grain production averaged 28–74% greater than the mean canola yields obtained in four regions of the NWCVT. Using the 2017 USDA canola price of $15.62/hundredweight (cwt; adjusted on geographical basis), returns for canola were projected to be $420 per acre. The study suggests that canola may be successfully incorporated into the region's crop rotations if trial yields can be matched in a commercial setting and processing/distribution channels can be firmed up.
Canola, an edible type of rapeseed and a member of the Brassica genus, was developed by Canadian plant breeders in the 1970s (Canola Council of Canada, 2017). Unlike other rapeseed cultivars, canola has lower levels of erucic acid
Figure 1. Flower cluster of a canola (Brassica napus) oilseed crop grown at the NMSU Agricultural Science Center in Farmington, New Mexico.
Title Opportunities to produce Canola in northern New Mexico: Results of field variety trials
Series Designation Research Report 793
Description Report examines potential returns from producing canola in semi-arid northern New Mexico based on study conducted at the New Mexico State University Agricultural Study Center at Farmington (ASCF).
Subject canola (NAL); oilseed crops (NAL); New Mexico;
Creator O'Neill, Michael K.; Lillywhite, Jay M.; Hawkes, Gerald; Trillanes, Mario; Djaman, Koffi
Collection NMSU Cooperative Extension Service and Agricultural Experiment Station Publications
Digital Identifier UAAPr000793.pdf
Source https://aces.nmsu.edu/pubs/
OCR 1Respectively, Professor (retired), Department of Plant and Environmental Sciences (PES); Professor, Department of Agricultural Economics and Agricultural Business (AEAB); Professor, AEAB; Graduate Student, AEAB; and Assistant Professor, PES, New Mexico State University. COLLEGE OF AGRICULTURAL, CONSUMER AND ENVIRONMENTAL SCIENCES aces.nmsu.edu/pubs • Agricultural Experiment Station • Research Report 793 Opportunities to Produce Canola in Northern New Mexico: Results of Field Variety Trials Michael K. O'Neill, Jay Lillywhite, Gerald Hawkes, Mario Trillanes, and Koffi Djaman1 New Mexico State University aces.nmsu.edu The College of Agricultural, Consumer and Environmental Sciences is an engine for economic and community development in New Mexico, improving the lives of New Mexicans through academic, research, and Extension programs. ABSTRACT Canola (Brassica napus; Figure 1), introduced by Canadian agronomists in the 1970s, has become an important agronomic crop for several states in the U.S. Producers from traditionally non-canola-producing regions are exploring opportunities to incorporate canola into their crop rotations. This study examined potential returns from producing canola in semi-arid northern New Mexico at the New Mexico State University Agricultural Science Center at Farmington (ASCF). The study used six-year variety trial results from plots managed by ASCF researchers compared to results from the National Winter Canola Varity Trial (NWCVT) over the same six-year period. Canola crop cost and return budgets were compared with other crops grown in the region. The greatest canola grain yields at ASCF were 4,150 lb/acre during the 2008/09 season and 4,321 lb/acre during the 2011/12 season. Over the length of the test, canola grain production averaged 28–74% greater than the mean canola yields obtained in four regions of the NWCVT. Using the 2017 USDA canola price of $15.62/hundredweight (cwt; adjusted on geographical basis), returns for canola were projected to be $420 per acre. The study suggests that canola may be successfully incorporated into the region's crop rotations if trial yields can be matched in a commercial setting and processing/distribution channels can be firmed up. INTRODUCTION Canola, an edible type of rapeseed and a member of the Brassica genus, was developed by Canadian plant breeders in the 1970s (Canola Council of Canada, 2017). Unlike other rapeseed cultivars, canola has lower levels of erucic acid Figure 1. Flower cluster of a canola (Brassica napus) oilseed crop grown at the NMSU Agricultural Science Center in Farmington, New Mexico.
Opportunities to produce Canola in northern New Mexico:...


Sultanpur Kendriya Vidyalaya Fees Details: Now Kendriya Vidyalaya Sangathan (KVS) has been collecting kendriya vidyalaya fees through kv online fee/ kvs fee challan by Union Bank of India (UBI). All Students who has taken admission in Kendriya Vidyalaya Tenga Valley, can download KVS Fee Challan from the official website of Union Bank of India or KVS.
The KVS Challan Form 2018 is available on the Union Bank of India fee collection web portal system for kv online fee payment. The Students or Parents of children studying in Kendriya Vidyalaya Tenga Valley can take kvs fee challan from themselves by logging on to the Uniun Bank of India's or the Kendriya Vidyalaya Sangathan's website because Kendriya Vidyalaya Tenga Valley has stopped distributing Kvs Fee Challan.
The Sultanpur Kendriya Vidyalaya Fee has been collected by Union Bank of India through various mode of KV School Fees Payment system i.e.
Kendriya Vidyalaya Fee Payment through PoS.
Note: KV Fee payment made through any bank credit card at Online KV School Fees Payment web portal or at PoS Machine installed in identified Kendriya Vidyalayas will add transaction charges @1.5% plus taxes only.
Generally, the Kendriya Vidyalaya Fees are collected by UBI on behalf of KVS on quarterly basis during the calendar months of January, April, July, and October each academic session. The Kendriya Vidyalaya School Fee will be collected from 1st to 15th of the above mentioned calendar month without any late fine.
Thereafter, system generated late payment fine will be applicable on KV Schools Fee collected from 16th to the last date of the kvs fee month of January, April, July and October.
Go to official website of Union Bank of India/ KVS. The link is given below in this page.
Then login using your students Unique ID (UID) and Date of birth as given by Kendriya School Sultanpur.
After a successfully Kendriya Vidyalaya fee payment, the students will be required to submit the KV Online Fee receipt/ Kendriya Vidyalaya Fees Challan to respective class teacher.
Note: If you have any queries regarding Kendriya Vidyalaya Fee Payment/ KV Online Fee Payment/ KV Online Fee/KV Challan Please contact kvhelpdesk@unionbankofindia.com.


text:
using System;
using System.Globalization;
using System.Text.RegularExpressions;

namespace _19.ExtractDates
{
    //Write a program that extracts from a given text all dates that match the format DD.MM.YYYY. Display them in the standard date format for Canada.

    class Program
    {
        static void Main(string[] args)
        {
            string text = @"Today on 12.11.1988 something happened that changed the events on 05.02.2011. Краен срок: 23:59 часа на 31.01.2013.
 Видео - 22.01.2013.
 Видео - 23 януари 2013 - Наков.
 Видео - 23.01.2013 - Наков.
 Краен срок: 23:59 часа на 03.02.2013.";

            foreach (var item in Regex.Matches(text, @"\w+\.\w+\.\w+")) // There are still some things that are not working 100%, like it counts a month "00" as valid.. there are regex-es that will do this better.
            {
                Console.WriteLine("{0}", item, CultureInfo.GetCultureInfo("en-CA"));
            }
        }
    }
}

redpajama_set_name: RedPajamaGithub


text:
By Jane Marion
After losing his daughter to an overdose, artist Peter Bruun is buoyed by ink, watercolors, and love.
When their daughter Elisif died of a heroin overdose five years ago, Baltimore artist Peter Bruun and his wife, Serafina, were flooded with support. "Devastated as my wife and I were, we were inundated with love from every direction," says Bruun. "We crowd-surfed our way on their love for months—and I don't know how we could have gotten by without that."
In fact, it's been love that has turned his pain into purpose and was the genesis of his latest exhibition, "Beyond Beautiful: One Thousand Love Letters," in which Bruun grapples with a new understanding of love and looks at the limitless ways to express it. The exhibit is on display at Area 405 and Maryland Art Place and will run through mid-March. By soliciting love letters from friends, and even using the words of historical figures as his muse, Bruun set out to explore—and draw—the many forms and faces of love. And not all of it is easy. From victims of sexual assault to a humorous love letter that Marlon Brando once wrote to a stewardess on a cocktail napkin, Bruun has found some catharsis through others sharing their stories. "I've always used my art to make sense of the world," says Bruun. "After my daughter died, with everything turned upside down, I asked myself, 'Why am I on this Earth?' Her passing was a challenge to any reason I'd given myself in the past. Three years ago, I realized that I'm here to love. I've begun to consciously think about my art as a way to delve into love."
Although Bruun admits that the act of visually rendering 1,000 letters has been a test of artistic endurance, the number is symbolic. "It's a symbol of the infinite," he says. "Like any marathon, you go to the water stop to refuel. Sometimes I'd get a new brush or new ink to spark my artistic interest beyond the letters, but, of course, the letters themselves kept me inspired."
For this project, Bruun also has contributed several letters of his own. For letter number 264, he used as his inspiration a note he wrote to Elisif when she was in the throes of addiction. "I wrote about a litany of things that were pushing the love away," says Bruun. "'Stay with me. Go away from me. Shut me out. I still love you. Berate me. Praise me. Cut me to the quick. I love you. I love you.'" And though he says that he's chronically healing, other people's letters have helped ground him. "My relationships with people have taken entirely unexpected paths," says Bruun. "The strangeness of my ride has felt disorienting. This project has affirmed the 1,000 forms that love can take. And there's really nothing abnormal about this ride that I've been on."
Number 495: Dick to Bonnie
This drawing is inspired by a Valentine's day letter that York, PA resident Brooke Hoffman's grandfather wrote to her grandmother, his wife of more than 50 years. The drawing, with its overlapping shapes, represents a symbol of long-lived love.
Number 209: Frida to Diego
This drawing is inspired by a letter from artist Frida Kahlo to her husband, Diego Rivera, a famed Mexican muralist. Throughout their long tempestuous relationship, each had affairs with others, and yet each continued to hold the other in the highest light.
Number 287: Elizabeth to Jacob
Famed photographer Jacob Riis was a Danish immigrant who carried for years a lifetime love for childhood friend, Elisabeth Giørtz, whom he had courted unsuccessfully before coming to America in 1870. Twelve years later, she finally wrote from Denmark the letter Riis had been waiting years to receive.
Number 121: Christy to Calvin
Christy has a son with a neurological disability, and she keeps a blog about him, so she is particularly mindful of the value of mothering and family. This drawing was inspired by Christy's relationship with her son.
Number 710: Michelle to B.
In this drawing, Bruun draws two figures walking through Central Park arm in arm, depicting a moment between a woman and her husband. This is her love letter to him.
Number 598: Margaret to Ruth
The words of anthropologist Margaret Mead to her mentor and lover, folklorist Ruth Benedict, inspired this piece: "I was simply thinking of hair as a beautiful thing," says Bruun.
Number 152: Abigail to John
Abigail and founding father, John Adams kept up an intimate relationship from long distance with countless letters, as she minded their home in Massachusetts—and he helped form a new nation. The letter inspiring this drawing was written on December 23, 1782, while the Revolutionary War still raged. In it, Bruun creates layered lines reminiscent of a fully grown and simply upright hedge—a metaphor for their marriage.
Number 122: Christy to Dear Mother
"I was thinking about two souls entwined as one, like mother-daughter—like veins and arteries taking and giving and receiving—like DNA strands wrapped," says Bruun of this piece. The piece was inspired by a woman's blog on the one-year anniversary of her mother's death.
Number 603: John to Colin
This depiction was inspired by an excerpt from My Dear Boy: Gay Love Letters through the Centuries, edited by Richard Norton. Colin Spencer was a novelist who fell in love with theater director John Tasker. They exchanged passionate letters until Spencer married a woman and suppressed his love for Tasker.
Number 96: Dad to Beth
Beth Fredrick—who now lives in Baltimore and travels the world for her work with Johns Hopkins University Bloomberg School of Public Health—grew up on a farm in Wisconsin. This drawing is inspired by a postcard Beth received from her father shortly after moving East in the 1980s. "In this drawing, I was thinking of Beth as the abstract figure on the right, sitting tall and proud, as her father—on the left—reaches toward her, with all the warmth and verdant colors of farm country in his words."
Find the Best in Baltimore
Redefining Music Education in Baltimore
The Baltimore School of Music shares their top community initiatives and how they extend music appreciation to all ages.
Music For The Future
A new Peabody major teaches students how to compose for an ever-changing media landscape.
MaxSpace
Movie Review: Marriage Story
All divorces are sad in different ways.
Music Reviews: December 2019
The latest from Al Rogers Jr. and The High & Wides.
Building Success: Behind the Business
The Doll House Boutique sells one-of-a-kind fashions with support from M&T Bank.
The Top Baltimore Music Moments of 2019
This year, the city's music scene came of age.
Movie Review: Knives Out
Finally, something the entire family will agree on this Thanksgiving!
Twenty of the most pivotal events in Baltimore this year, in chronological order.
Gervonta Davis Wins Another World Title Belt, Eyes 'Big Year' Ahead: West Baltimore's own was bruised, but not beaten in longest fight of his pro career.
Will Judge Make an Example Out of Catherine Pugh?: With the former mayor's sentencing scheduled for February, both sides get to work on their case.
Maryland Native Maggie Rogers Receives First Grammy Nomination: The singer-songwriter was nominated for "Best New Artist" alongside big-name acts.


The owner of an Elkridge massage parlor who prosecutors allege tried to bribe police and a state inspector as part of a scheme to take over his competitors was arrested yesterday after a 14-month investigation.
SungYul Kim, 65, of Fort Lee, N.J., was taken into custody by Howard County police at a Dorsey motel under a sealed, three-count indictment issued Feb. 11, said State Prosecutor Stephen Montanarelli, whose office led the investigation. The misdemeanor charges were unsealed with his arrest, and convictions could lead to prison terms of up to 36 years and fines up to $15,000.
Kim, who operates the Oriental Spa in the 6300 block Washington Blvd., was ordered held without bail after a brief hearing in Howard County Circuit Court. Three women working in his spa were arrested on immigration violations during a search of the business, Montanarelli said.
Kim stood shackled at the ankles as Thomas M. McDonough, senior assistant state prosecutor, told Howard Circuit Judge Dennis M. Sweeney that the massage parlor operator "has ready assets of over $2 million," has traveled back to Korea and to Libya several times in recent years, owns very little if any property in the United States and "will not appear for trial" if he is granted bail.
According to the indictments, Kim tried to recruit Paul Murphy, a state inspector who helps regulate massage parlors for the Maryland Chiropractic Board, into a scheme to pressure competing parlor owners so Kim could buy their businesses for low prices.
Murphy approached Montanarelli's office in November 2002, and two Howard County police officers - Lt. Keith Lessner and Detective Guy Williams - were brought in to pose as Murphy's accomplices in corruption.
Montanarelli said that during the investigation, Kim gave the men "very expensive" women's jewelry and "substantial amounts" of cash, though he refused to provide details. Kim is also said to have provided liquor and meals, hoping that the officials would harass operators of other spas so they would be eager to sell. The police used "electronic surveillance" to help gather evidence, Montanarelli said.
Kim appeared confused at the hearing and, although he became a U.S. citizen in 1979, he told Judge Sweeney that he speaks only "a little bit" of English. McDonough disputed that. A second hearing was scheduled for March 5 with a Korean interpreter in attendance.
McDonough also said Kim wanted to buy forged immigration documents from the officers and had one prior conviction for "unlawful sexual contact" in New Jersey.
Howard police have alleged the massage parlors involved in the case are prostitution operations, and raided six of them - including Oriental Spa - in a 1995 sting operation. But the cases fell apart in the face of charges that officers engaged in sexual acts to obtain evidence.


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Singapore – October 28, 2016 – BASF today launched MasterEase®, a next-generation admixture for production of low-viscosity concrete, for the Singapore market. The new admixture range improves the rheological properties and the stability of high-performance concrete.
This new generation of polymers reduces the plastic viscosity of concrete by up to 30 percent. In addition, it reduces the time and effort for pumping and placement of concrete, making installation much easier and faster.
"Innovation is an important driver for our long-term growth. We collaborate with our customers to continuously work on new products and solutions as well as innovative technologies. With the launch of MasterEase, we are offering our most advanced, and globally approved products in Singapore to support our customers in overcoming construction challenges and to successfully realize their projects," said Arnold De Silva, Head of BASF's Construction Chemicals Division for Singapore, Malaysia, Myanmar, Laos and Cambodia.
Concrete producers and users will benefit in many ways from the new technology, which is flexible and can be adapted to challenging situations such as temperature and material variations. It is particularly suitable for concrete mixes which are designed and optimized for advanced engineering properties and sustainability. For example, high-strength concrete with low water/cement ratios, and mixes with higher levels of secondary cementitious materials reduce the CO2 footprint.
"Singapore is one of the leading countries in Asia in enhancing construction productivity through strong regulatory framework. We want to be the partner of choice in providing advanced technologies to further increase efficiency and support sustainable development across the industry. MasterEase makes it easier to produce and place ready-mix concrete and this helps engineers and developers to improve speed, durability and sustainability ratings of their projects," said Daniel Loh, Managing Director, BASF (Malaysia) Sdn Bhd and Head of Malaysia-Singapore Business Area.
During the launch, 50 guests were invited to a demonstration of the advantages of MasterEase to experience the quality of the new technology up-close.
BASF's Construction Chemicals division offers advanced chemicals solutions for new construction, maintenance, repair and renovation of structures: Our comprehensive portfolio encompasses concrete admixtures, cement additives, chemical solutions for underground construction, waterproofing systems, sealants, concrete repair & protection systems, performance grouts, performance flooring systems, tile fixing systems, expansion control systems and wood protection solutions.
The Construction Chemicals division's about 5,500 employees form a global community of construction experts. To solve our customers' specific construction challenges from conception through to completion of a project, we combine our know-how across areas of expertise and regions and draw on the experience gained in countless construction projects worldwide. We leverage global BASF technologies, as well as our in-depth knowledge of local building needs, to develop innovations that help make our customers more successful and drive sustainable construction.


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text:
| City campaign targets alcoholism, drug addiction
Stephen Butler
City campaign targets alcoholism, drug addiction
Stephen Butler 12:00 am, Sep 24, 2003
September, for many New Haven residents, is a time for barbecues, raking leaves, and absorbing the last few warm days before the temperature drops. But the month also marks the 14th annual National Alcohol and Drug Addiction Recovery Month, and the city's Office of Substance Abuse Policy and Prevention has launched a campaign to highlight the ongoing struggles of alcoholism and drug addiction.
Hoping to reach out to New Haven residents from all socioeconomic backgrounds, the Office of Substance Abuse Policy and Prevention, or OSAPP, has planned a monthlong radio advertising campaign on WYBC, released an endorsement from New Haven Mayor John DeStefano Jr., and sent eight busloads of people to a "Recovery Walk" in Hartford last Sunday.
The city of New Haven is participating in September's activities in order to inspire substance-dependent and other residents to work toward a positive future, OSAPP director Esther Armmand said.
"Our main purpose is to heighten awareness of [substance abuse] as a problem," Armmand said. "It affects all social classes and cultural groups."
One aspect of OSAPP's outreach involves distribution of their "Summer Buzz" pamphlet, a sequel to the acclaimed "Buzz without the Booze" publication, which contains recipes for exotic nonalcoholic mixed drinks.
Substance-abuse is an ongoing, widespread medical problem to which events like the Hartford walk help to draw attention, said Phillip Valentine, associate director of the Connecticut Community for Addiction Recovery.
"I think the problem [of substance abuse] itself is about the same [as it has ever been,]" Valentine said. "[But] the way we're addressing it has radically changed; our prisons are swelling with nonviolent people — I think these people are sick and need treatment. If thousands of us have put our faith in recovery, people will see hope."
This month's events will also help reduce the stigma sometimes associated with substance abusers, Armmand said.
"We want people to learn that the only shame is not seeking help," she said.
In New Haven, Hartford, and cities across the country, substance abusers and their supporters have planned and attended Recovery Month events.
Alisha Pollastri, a staff affiliate in the Psychiatry Department of Yale Medical School, attended Sunday's walk in Hartford along with others from Yale's research program on the genetics of substance dependence. Pollastri said she and her coworkers participated in the walk with two purposes in mind: to recruit new participants for Yale research studies and to offer reaffirmation to their patients.
"We work day to day with people who are struggling with recovery and we wanted to show our support," Pollastri said.
Student health educator and associate clinical professor of psychiatry Carole Goldberg said Yale's student health education program does not traditionally participate in Recovery Month.
"We focus more on the entire year," Goldberg said. "We've found that it works better for the students."
Goldberg also said a Student Health Expo will be held tonight at Yale University Health Services, and a substance-abuse counselor will be available there for discussion.


text:
var path = require('path');
var spawn = require('cross-spawn');
var script = process.argv[2];
var args = process.argv.slice(3);

// Note that `npm run dev` is concurrently running `server` and `dev-server`
switch (script) {
case 'build':
case 'server':
case 'dev-server':
case 'css-formatting':
case 'eject':
  spawn(
    'node',
    [path.resolve(__dirname, '..', 'scripts', script)].concat(args),
    {stdio: 'inherit'}
  );
  break;
default:
  console.log('Unknown script "' + script + '".');
  console.log('Perhaps you need to update expressful-tools?');
  break;
}